{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.02985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.430</td>\n",
       "      <td>58.7</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.12</td>\n",
       "      <td>5.21</td>\n",
       "      <td>28.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.08829</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.012</td>\n",
       "      <td>66.6</td>\n",
       "      <td>5.5605</td>\n",
       "      <td>5</td>\n",
       "      <td>311</td>\n",
       "      <td>15.2</td>\n",
       "      <td>395.60</td>\n",
       "      <td>12.43</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.14455</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.172</td>\n",
       "      <td>96.1</td>\n",
       "      <td>5.9505</td>\n",
       "      <td>5</td>\n",
       "      <td>311</td>\n",
       "      <td>15.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>19.15</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.21124</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>5.631</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6.0821</td>\n",
       "      <td>5</td>\n",
       "      <td>311</td>\n",
       "      <td>15.2</td>\n",
       "      <td>386.63</td>\n",
       "      <td>29.93</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.17004</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.004</td>\n",
       "      <td>85.9</td>\n",
       "      <td>6.5921</td>\n",
       "      <td>5</td>\n",
       "      <td>311</td>\n",
       "      <td>15.2</td>\n",
       "      <td>386.71</td>\n",
       "      <td>17.10</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM    AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575   65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421   78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185   61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998   45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147   54.2  6.0622    3  222     18.7   \n",
       "5  0.02985   0.0   2.18     0  0.458  6.430   58.7  6.0622    3  222     18.7   \n",
       "6  0.08829  12.5   7.87     0  0.524  6.012   66.6  5.5605    5  311     15.2   \n",
       "7  0.14455  12.5   7.87     0  0.524  6.172   96.1  5.9505    5  311     15.2   \n",
       "8  0.21124  12.5   7.87     0  0.524  5.631  100.0  6.0821    5  311     15.2   \n",
       "9  0.17004  12.5   7.87     0  0.524  6.004   85.9  6.5921    5  311     15.2   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  \n",
       "5  394.12   5.21  28.7  \n",
       "6  395.60  12.43  22.9  \n",
       "7  396.90  19.15  27.1  \n",
       "8  386.63  29.93  16.5  \n",
       "9  386.71  17.10  18.9  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "boston = pd.read_csv('./data/boston_house_prices.csv')\n",
    "boston.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x2132ecce978>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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zy33HHXecDh06VIRWFZfH73nGpR5OHzXOLXjgl1m9ZgkM32V0/r7NKTnndkoan+T4fkm+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\n/j9GAVAoHAkQfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2132ecce278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter\n",
    "\n",
    "sns.jointplot(x = 'CRIM', y = 'MEDV', data = boston)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2132edc59e8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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//fPD/Nt/n+C29U28b9UiPK7cgmnCsukOxekOxQl4XJT6XJQUYaGHrIWLiSje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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2132e86b668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linear regression\n",
    "\n",
    "sns.lmplot(x = 'CRIM', y = 'MEDV', data = boston)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2132f22b630>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vobZV1ANMaf/EoORp0eSmxLHtUP2UnzsUeZModgMlIlIsIjG4O6e3DCmzBbjd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\nKIwxxozKEoUxxphR/X+faKi2InyBvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2132f2054a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hist with kde\n",
    "sns.distplot(boston['TAX'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2132f41b2b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uTLIvyYNJ3tW1n5rkjiSPdI+ndO1J8pHusgz3JTlneUewNElenOSuJN/qxvlvu/Yzk+zt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\n7VZrVieXscQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2132f4632e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hist without kde\n",
    "sns.distplot(boston['TAX'], kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2132f517198>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n+rzWQh+5ALgkycOMrhp5UZJ/mm6k4VTVI93yEKP51nOnm2gQB4GDVbWnW7+NUcG36i3A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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2132f4dc6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(boston[\"RM\"], kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2132f5f1358>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qzHE3crffsNGRR/psUR1fjtbRlNN2pL/Ds3j277DrO8rPYDX+BkfJiElyZNi55v17Z49f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\nkhpj8EtSY/4/Qn8h/fpx0wwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2132f4dcda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(boston[\"MEDV\"], bins = 100, kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
